The granulocyte is absolutely essential for host defense and survival. Its pathophysiological importance is apparent in severe congenital neutropenia (SCN). Life-threatening infections in children with SCN can be avoided through the use of recombinant granulocyte colony-stimulating factor (GCSF), which increases the number of granulocytes. However, SCN often transforms into myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). A great unresolved clinical question is: do chronic, pharmacologic doses of GCSF contribute to this transformation. Two major sets of human clinical and experimental data strongly suggest such a linkage. First, a number of epidemiological clinical trials have demonstrated a strong association between exposure to GCSF and MDS/AML. Second, mutations in the distal domain of the GCSF Receptor (GCSFR) have been isolated from 70% of patients with SCN who developed MDS/AML. Most recently, clonal evolution over ~20 years was documented in a patient with SCN who developed MDS/AML. What is very striking is that five different mutations arose in the GCSFR gene, one persisted into the AML clone but others became extinct during the course. We hypothesize that clonal evolution of an SCN sick stem cell involves perturbations in proximal and distal signaling networks triggered by a mutant GCSFR. Transition from SCN?MDS?AML most likely also depends on chance, hence the need for a stochastic model. To address these hypotheses through computational modeling and experimental validation, we propose the following specific aims: Aim 1) Develop and evaluate a network model to account for the dynamics of normal and aberrant GCSFR signaling effects and their interactions with mutant ELANE; and Aim 2) Estimate the number, timing, and selective advantage of mutations in granulocyte progenitors at the MDS/AML stages and develop and validate population genetics models to predict risk of transition from SCN?MDS?AML. To accomplish these aims, we have assembled an expert multidisciplinary team in state-of-the-art experimental hematology (high-dimensional mass cytometry, cellular barcoding, and patient-derived iPSC), computational biology, network analysis, and applied probability to develop an innovative multiscale systems analysis of how defective granulopoiesis undergoes malignant transformation. Our goal is to produce a first-generation, multi-scale model for clonal evolution of a sick blood stem cell into an unstable one. Our long-term objectives are to establish patterns of network perturbations in myeloid clonal evolution, predict patient risk fo transformation, and design measures to prevent that life-threatening event. One insight from our modeling is that we can predict when transformation to MDS can occur in patients with SCN, which could be used to optimize surveillance and clinical intervention.